Effect of an intensive comprehensive aphasia program on language and communication in chronic aphasia
Bibliographic record
Abstract
Intensity has been identified as an important determinant of treatment effectiveness in aphasia, especially in the chronic phase. Intensive Comprehensive Aphasia Program (ICAP) is a popular treatment delivery model that includes an array of treatment approaches provided at a high level of intensity. Only a few studies have reported the effect of ICAPs on language and communication so far. The effect of intensity on different interventions provided as part of the program should also be studied to optimize the delivery schedule. The first aim of this study was to measure the effect of an ICAP for people with chronic aphasia using consensus outcome measures and other standard tests. The second aim was to test the effect of intensity on naming treatments provided during the ICAP. People with aphasia (n = 7) who had their strokes at least 6 months before the beginning of the study attended an ICAP that was provided for 4 hours a day, 3 days a week over 4 weeks (48 hours in total). On each treatment day, participants received 2 hours of individual therapy, 1 hour of technology-based therapy, and 1 hour of group therapy. Individual therapy included a naming treatment using two equivalent lists of words. One list was treated once a week for 4 weeks while the other was treated on four consecutive treatment days. Lists were controlled for word length, word frequency, number of words from a specific semantic category, and average naming accuracy at baseline. Both lists were treated using the same approach. As a group, participants had higher total scores on the Boston Naming Test (BNT) after attending the ICAP. The change on the Western Aphasia Battery – Revised Aphasia Quotient (WAB-R AQ) was not significant. No change was observed on measures of emotional well-being, quality of life, and functional communication. Individual comparisons using published benchmarks for significant change following aphasia treatments showed significant changes in all participants on the BNT, the WAB-R AQ, or both. Participants made significant naming gains for words treated once a week over four weeks and words treated on four consecutive treatment days. There was no difference between the two naming treatment schedules. All participants made measurable language gains following their participation in an ICAP. More studies are needed to understand the effect of intensity on components of ICAPs and to apply this knowledge to optimize treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".